Preeti
Retrieval Augmented Generation (RAG) is a method that enhances Large Language Models (LLMs) by providing them with private or additional data not available during training. This approach improves the accuracy of LLM responses to user queries, addressing limitations such as static training data and lack of detailed knowledge over time. For instance, in creating a chatbot for a company, RAG can enable the bot to access private data and confidential information not available on the internet without retraining the LLM. By adding context like a fictional user manual for a washing machine, the LLM can provide more accurate responses based on this new information.
The author created a simple RAG called “tinyrag” to demonstrate how providing additional data can enhance LLM answers. By storing a fictional user manual in a vector database and coding a LangChain application, the author showed how the LLM’s responses differ with and without context. For instance, when asked about washing programs for the Potter 3000 washing machine, the LLM without context provided a generic response, while with context, it accurately listed the specific programs and settings for the machine.
This experiment showcases how RAG can improve LLM responses by incorporating additional data, enabling more precise and detailed answers to user queries. By providing context or additional information, LLMs can be enhanced to provide more accurate responses tailored to specific topics or scenarios.
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Source link: https://medium.com/@perepc/how-a-simple-rag-can-improve-llm-answers-3f850f4cd449?source=rss——ai-5
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